behavioral testing

**Behavioral Testing** of ML models is a **systematic approach to testing model behavior using input-output test cases** — inspired by software engineering testing practices, organizing tests into capability-specific categories to comprehensively evaluate model reliability. **CheckList Framework** - **Minimum Functionality Tests (MFT)**: Simple test cases that every model should handle correctly. - **Invariance Tests (INV)**: Perturbations that should NOT change the prediction. - **Directional Expectation Tests (DIR)**: Perturbations that should change the prediction in a known direction. - **Test Generation**: Use templates, perturbation functions, and generative models to create test suites. **Why It Matters** - **Beyond Accuracy**: Accuracy on a test set doesn't reveal specific failure modes — behavioral tests do. - **Systematic Coverage**: Tests cover linguistic capabilities, robustness, fairness, and domain-specific requirements. - **Regression Testing**: Behavioral test suites catch regressions when models are retrained or updated. **Behavioral Testing** is **test-driven development for ML** — systematically testing model capabilities, invariances, and directional expectations.